Fuzzy Scatter Matrix Based Class Separability Criterion

Su Qun Cao; Yun Feng Bu

doi:10.4028/www.scientific.net/AMR.179-180.409

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 179-180Fuzzy Scatter Matrix Based Class Separability...

Fuzzy Scatter Matrix Based Class Separability Criterion

Abstract:

Scatter matrix based class separability criterion is commonly used in supervised feature extraction. But calculations of scatter matrixes depend on labeled data, so this criterion can not be used in unsupervised pattern. This paper presents a method to extend scatter matrix based class separability criterion to unsupervised pattern by fuzzy theory. The basic idea is to optimize the defined fuzzy Fisher criterion function to figure out fuzzy scatter matrixes in unsupervised pattern. Based on the obtained fuzzy between-class scatter matrix and fuzzy within-class scatter matrix, a novel class separability criterion based unsupervised feature extraction is proposed. Experimental results on its applications in UCI datasets show its effectiveness.

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Periodical:

Advanced Materials Research (Volumes 179-180)

Pages:

409-414

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.179-180.409

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Online since:

January 2011

Authors:

Su Qun Cao, Yun Feng Bu

Keywords:

Feature Extraction, Fuzzy Scatter Matrix, Separability Criterion, Unsupervised Pattern

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